Implied Volatility Measures Research Articles

The Federal Reserve’s Quantitative Easing policy and volatility spillovers: Evidence from Australia

The Federal Reserve’s Quantitative Easing (QE) policies have injected significant liquidity into financial markets. While a vast literature examines the implications of QE, its effect on the financial markets of relatively small open economies is yet to be fully understood. This paper uses Australia as a case study to reveal evidence of intensified volatility spillovers resulting from QE. Using ex-ante implied volatility measures, we derive time-varying indices that exhibit the risk transference between financial markets in Australia and the U.S. Our results show intensified spillovers explainable by QE, especially for equity markets during periods of policy normalization. An amplification of spillovers is also found to follow contractionary QE shocks, implying that risk increases as asset purchases decline. In all, we provide evidence of an international volatility channel of unconventional monetary policy, demonstrating the ramifications of central bank-induced portfolio balancing among investors.

Financial market discipline on bank risk: Implications of state ownership

This study investigates the link between capital market discipline and bank-level credit risk with a special emphasis on the role of bank ownership structure. Focusing on a large emerging market, Türkiye, characterized by a prominent state bank presence, our baseline regression results indicate that banks' stock price volatility elevates in response to the increases in non-performing loan ratio for the period 2008–2021. More importantly, the extent of capital market discipline on credit risk is amplified for state-owned banks. This finding remains similar against a myriad of robustness checks. To analyze the implications on alternative financial markets, we further extract high-frequency implied volatility measures from options contracts recently traded on individual bank stocks. By utilizing the Covid-19 outbreak as an exogenous shock to local banks’ loan portfolio quality, we perform difference-in-differences estimations for the interval of October 2019–June 2020. Our findings show that the implied volatility for non-private banks increases more in the post-shock phase compared to other bank ownership types.

Implementing Intraday Model-Free Implied Volatility for Individual Equities to Analyze the Return–Volatility Relationship

We implement the VIX methodology on intraday data of a large set of individual equity options. We thereby consider approaches based on monthly option contracts, weekly option contracts, and a cubic spline interpolation approach. Relying on 1 min, 10 min, and 60 min model-free implied volatility measures, we empirically examine the individual equity return–volatility relationship on the intraday level using quantile regressions. The results confirm a negative contemporaneous link between stock returns and volatility, which is more pronounced in the tails of the distributions. Our findings hint at behavioral biases causing the asymmetric return–volatility link rather than the leverage and volatility-feedback effects.

Volatility connectedness between global COVOL and major international volatility indices

This research explores dynamic connectedness amongst financial markets using the novel financial risk measure, global common volatility (COVOL) (Engle and Campos-Martins, 2023) and four major asset price implied volatility measures using a TVP-VAR framework. Considering several major international crises, results show that COVOL is a shock receiver while also identifying that VIX is a dominant risk driver of the pricing of risk transmission for the global financial system. The fluctuations of connectedness between COVOL and each implied volatility indices are highly dependent on periods of exceptional stress across international financial markets.

REIT Sector Implied Volatility Index: Liquidity and Information of Option Trading

An accurate volatility forecast is essential in financial investments and risk management. Existing literature finds that the implied volatility from options trading best predicts the realized volatility in various financial products. In 2016, real estate was added to the S&P 500 as the eleventh sector; however, a reliable implied volatility measure for the real estate sector has not been developed yet. The existing literature in real estate investment trusts (REITs) relies primarily on insufficient volatility forecasts, such as implied volatility for a broader market or time series analysis. In this research, we develop a REIT sector implied volatility index derived from options on the US Real Estate exchange-traded fund, IYR. As Whaley, the creator of the VIX (implied volatility on S&P 500), points out, enough liquidity in option trading is critical in developing an implied volatility index. This study shows that IYR option trading is liquid enough and informative that our REIT sector implied volatility index outperforms other volatility forecast measures. Our findings suggest that the REIT sector implied volatility index from option trading data can be utilized in future research and industry risk management.

Variation in option implied volatility spread and future stock returns

A consistent theme in the finance literature is that implied volatility measures have predictive power for future stock returns and thus reflect investor beliefs about future stock valuations. We use the variability of option implied volatility spread as a proxy for the impounding of new information, and changes in the interpretation of existing information, into option prices. Over the 2006–2016 period, we find that the predictive power of option implied volatility spread for future stock returns is significantly greater when implied volatility spread has been more variable in the past. Our results are statistically and economically significant and robust in both univariate and multivariate settings. Overall, our findings suggest a portfolio strategy using the conjunction of both the level and variability of the implied volatility spread results in significantly better portfolio performance than using the level alone.

Spreading the fear: The central role of CBOE VIX in global stock market uncertainty

Construction of efficient portfolios is reliant on understanding the correlation between assets. If correlations change markedly during times of economic turmoil then investors are exposed to greater risk at the most inopportune time. We examine the linkages between global stock markets using measures of market uncertainty (implied volatility). Using a sample of daily changes in G7 and BRIC implied volatility measures, over a 20-year sample period, we demonstrate that uncertainty in U.S. markets plays a pivotal role in global stock market uncertainty. “Fear is spread” across markets, as heightened uncertainty in U.S. markets is transmitted across global markets. Conversely, changes in global market uncertainty do not explain changes in U.S. market uncertainty. While there is a clear increase in connectedness during crisis periods, we observe a disparity in the way that inter-dependencies change during the two major economic crises in our sample period; the GFC (2007–2009) and COVID-pandemic (2020). The additional importance of US news largely drives our results during the GFC, while the effect is spread among several countries (particularly within European markets) during COVID.

What matters when developing oil price volatility forecasting frameworks?

AbstractForecasting oil price volatility is considered of major importance for numerous stakeholders, including, policy makers, industries, and investors. This paper examines and evaluates the main factors that oil price volatility forecasters should consider before constructing their forecasting models. Such factors are related to (i) direct versus iterated forecasts, (ii) the incorporation of continuous and jump components, (iii) the importance of semi variance volatility measures, and (iv) ordinary least squares (OLS) versus time‐varying parameter (TVP) estimation procedures. We evaluate the performance of these factors for both the realized and implied volatility measures of the West Texas Intermediate (WTI) crude oil price, based on statistical loss functions, as well as their economic use. The results show that depending on whether end users are interested in forecasting the realized or the implied volatility, the factors influencing the accuracy of forecasts are different. In particular, for the realized volatility, direct forecasting based on TVP estimation procedure, as well as using the information obtained in the semi variance measures, is capable of producing significantly superior forecasts. By contrast, separating the continuous and the jump components of the realized volatility does not provide any added value to these forecasts. Turning to the oil price implied volatility index (OVX), based on the economic evaluation of our forecasts, the TVP estimation procedure seems to perform better. In addition, we find evidence that the continuous component and the semivariance measures of the realized volatility also yield better OVX forecasts in the longer run horizons.

Does happiness forecast implied volatility? Evidence from nonparametric wave-based Granger causality testing

Past studies on the causal relationship between online sentiment and implied volatility indices report mixed results. In this paper, we employ a daily happiness index extracted from Twitter as a proxy for online sentiment to investigate the relationship between sentiment and the Chicago Board of Option Exchange volatility indices. Applying a nonparametric wave-based Granger causality test, allows us close examination of frequencies and periods of causality. Investigating four different subsample periods, we evidence that daily happiness sentiment consistently manifests short-term causality of implied volatility indices. This is evidenced over our sample period as a whole, and for three out of four investigated subperiods—the exception being a subperiod of three years following the Global Financial Crisis. Additional Diebold and Yilmaz (2012) spillover testing evidences that the causal relationship between happiness and implied volatility is strongest for stable low happiness subperiods. Importantly, we also evidence intervals where spillovers from happiness to implied volatility spike sharply. This suggests that the relationship between happiness and implied volatility is heavily conditioned by exogenous factors. Consequently, while we identify consistent short-term causality of happiness to implied volatility measures, economically meaningful forecasting of implied volatility from happiness remains elusive. Our findings should be of considerable interest to investors and practitioners, as well as to behavioral finance researchers interested in how sentiment shapes the ‘uncertainty of uncertainty.

Global equity market leadership positions through implied volatility measures

Abstract Using unique minute-by-minute data on six major country implied volatility series, we examine the spillovers and the leadership positions of the global stock exchanges through measuring and assigning the contributions of innovations among their implied volatilities. The entire analyses are performed on synchronized transactions. A hybrid leadership methodology that is computationally efficient is employed. In nearly all cases, the findings indicate a clear relative leadership position for one or more exchanges. These, in turn, provide important insights into the operations of the markets and convey the dynamic process among them. We also address the transmission mechanism and volatility efficiency.

What Matters When Developing Oil Price Volatility Forecasting Frameworks?

Forecasting oil price volatility is considered of major importance for numerous stakeholders, including, policy makers, industries and investors. This paper examines and evaluates the main factors that oil price volatility forecasters should take before constructing their forecasting models. Such factors are related to: i) direct vs iterated forecasts, ii) the incorporation of continuous and jump components, iii) the importance of semi variance volatility measures, and iv) OLS vs time-varying parameter (TVP) estimation procedures. Even more, we evaluate the performance of these factors for both realized and implied volatility measures, based on statistical loss functions, as well as, their economic use. The results show that depending on whether end-users are interested in forecasting the realized or the implied volatility, the factors influencing the accuracy of forecasts are different. In particular, for the realized volatility, direct forecasting based on TVP estimation procedure, as well as, using the information obtained in the semi variance measures are capable of producing significantly superior forecasts. By contrast, separating the continuous and the jump components of the realized volatility does not provide any added value to these forecasts. Turning to the OVX, based on the economic evaluation of our forecasts, the TVP estimation procedure seems to performbetter. In addition, we find evidence that the continuous component and the semi variance measures of the realized volatility also yield better OVX forecasts in the longer run horizons.

Volatility forecasts embedded in the prices of crude‐oil options

AbstractThis paper evaluates the ability of alternative option‐implied volatility measures to forecast crude‐oil return volatility. We find that a corridor implied volatility measure that aggregates information from a narrow range of option contracts consistently outperforms forecasts obtained by the popular Black–Scholes and model‐free volatility expectations, as well as those generated by a realized volatility model. This measure ranks favorably in regression‐based tests, delivers the lowest forecast errors under different loss functions, and generates economically significant gains in volatility timing exercises. Our results also show that the Chicago Board Options Exchange's “oil‐VIX” index performs poorly, as it routinely produces the least accurate forecasts.

Volatility Forecasts Embedded in the Prices of Crude-Oil Options

This paper evaluates and compares the ability of alternative option-implied volatility measures to forecast the monthly realized volatility of crude-oil returns. We find that a corridor implied volatility measure that aggregates information from a narrow range of option contracts consistently outperforms forecasts obtained by the popular Black-Scholes and model-free volatility expectations, as well as those generated by a high-frequency realized volatility model. In particular, this measure ranks favorably in all regression-based tests, delivers the lowest forecast errors under either symmetric or asymmetric loss functions, and generates economically significant gains in volatility timing exercises. Our results also show that the CBOE's oil-VIX (OVX) index performs poorly, as it routinely produces the least accurate forecasts.

Asymmetric relationship between implied volatility, index returns and trading volume: an application of quantile regression model

The study explored a vital issue on market microstructure that is the relationship between volatility, index returns and trading volume within the Indian stock market. The daily closing prices of select indices, from 2 March 2009 to 31 July 2018, were taken. The investor’s fear index India Volatility Index (VIX) was used as an implied volatility measure popularly the investor’s fear index, whereas Nifty50 daily returns and trading volume were considered. Toda–Yamamoto causality test provided limited information about the causal relationship among the variables. The results of the Toda–Yamamoto test captured only the central values of the dependent variable’s distribution; therefore, quantile regression models (QRM) were estimated. Through QRM, the asymmetric impact of returns was estimated on volume and volatility changes. The asymmetric relationship of stock returns with changes in volatility and volume distribution was found significant. It was found to be stronger at extreme ends of the dependent variable’s distribution. The study supports the behavioural justification for a contemporaneous negative return–volatility relationship but conditionally. Evidence of investor’s heterogenous beliefs is found. The evidence of leverage effect was also significant for the lagged period. The contemporaneous negative relationship was found between volatility and volume changes highlighting that the investors in Indian markets are risk-averse. But the positive lagged effect of changes in volatility on trading volume supports sequential arrival of information hypothesis and affirmed the presence of noise traders. They make the information arrival sequential and allow the slow diffusion of information. India VIX potentially can be used for portfolio hedging purposes.

Information and volatility linkages across energy and financial markets

This study examines information and volatility linkages across energy and financial markets. In a world economy so connected, the impacts of climate change are likely to be transmitted through interlinked global markets. Hence, uncovering and understanding the interaction across these markets is a fundamental concern during the energy transition as it helps to understand how to strengthen incentives to facilitate energy investments. Based on the relation between information flows and volatility, this study employs a simple correlation approach based on implied volatility measures and the trading model of Fleming et al. to measure the common information linkages, as gauged by the correlation of return volatilities. The results suggest that volatility linkages across these markets are strong due to common information sharing and cross-market hedging. JEL Classification: G12, G14

Expected market returns: SVIX, realized volatility, and the role of dividends

SummaryThis note provides a replication of Martin's (Quarterly Journal of Economics, 2017, 132(1), 367–433) finding that the implied volatility measure SVIX predicts US stock market returns up to 12‐month horizons. I find that this result holds for both S&P 500 and CRSP market returns, regardless of whether returns include or exclude dividends. The predictability largely disappears after the SVIX index is replaced by an exponentially weighted moving average measure of realized volatility, suggesting that SVIX holds incremental forward‐looking information compared to realized volatility, despite the high correlation between the two volatility measures.

Spreading the Fear: The Central Role of Cboe VIX in Global Stock Market Uncertainty

Construction of efficient portfolios is reliant on understanding the correlation between assets. If correlations change markedly during times of economic turmoil then investors are exposed to greater than desired risk levels at the most inopportune time. We examine the linkages between global stock markets using measures of market uncertainty (implied volatility). Using a sample of daily changes in G7 and BRIC implied volatility measures, over a 20-year sample period, we demonstrate that uncertainty in U.S. markets plays a pivotal role in global stock market uncertainty. “Fear is spread” across markets, as heightened uncertainty in U.S. markets is transmitted across global markets. Conversely, global markets do not appear to explain innovations in U.S. market uncertainty. While there is a clear increase in inter-dependence during crisis periods, we observe a disparity in the way that inter-dependencies change during the two major economic crises in our sample period; the GFC (2007-2009) and COVID-pandemic (2020). The additional importance of US news largely drives the result during the GFC, while the effect is more widespread (particularly within European markets) during COVID.

Dynamic and directional network connectedness of crude oil and currencies: Evidence from implied volatility

We explore the dynamic and directional network connectedness between implied volatility measures of crude oil and the exchange rate of nine major currency pairs for a sample period from May 2007 to December 2016. We use the dynamic Cholesky-factor vector autoregression variance decomposition method. To ascertain the net directional spillovers and to determine the direction of the transmission of shocks, we employed the network graph connectedness method across a few select events characterized by the crude oil price volatility of 2008–09 and 2014–16. We found that the crude oil market has a dominant impact on the total connectedness of the crude oil currency-implied volatility relationship, suggesting that crude oil affects currencies more than currencies affect crude oil for the time frame of 2007–16. However, during the crude oil crisis periods, the dynamics are reversed, with crude having more of an effect. Additionally, the pairwise directional network connectedness between the currency pairs reveals that EURUSD is more sensitive to the crude price fluctuation than other major currency pairs and is one major currency that passes idiosyncratic shocks to other currency pairs.

Forecasting the KOSPI200 spot volatility using various volatility measures

This study examines the volatility forecasting performance of various historical and implied volatility measures. We compare the informational efficiency of lagged realized volatility, GARCH-family volatilities, out-of-the-money (OTM) and at-the-money (ATM) implied volatilities, and the market volatility index (VKOSPI) using univariate and encompassing regression analyses. We find that historical and implied volatility both have good predictive ability, but are biased estimators of future volatility. Furthermore, the information content of the implied volatility constructed from slightly OTM options encompasses that of the deep OTM and ATM options. In general, the VKOSPI exhibits the best forecasting performance among the volatility measures analyzed in this study. However, incorporating GJR–GARCH volatility, which exhibits the best performance among the GARCH-family volatilities, in the prediction model possibly improves the explanatory power of the VKOSPI.

Implied Volatility Measures As Indicators of Future Market Returns

We analyze the relationship between implied volatility and subsequent equity markets excess returns. We find that high readings of implied volatility have a strong correlation with positive and economically sizable returns in the subsequent 1, 5, and 20 trading days. Mid-level readings are generally associated with returns which are statistically positive but not economically significant. Low readings show a statistically meaningful link with negative but economically small returns. We also document that the statistical relationship with equity markets behavior is stronger when using the VIX as a measure of implied volatility instead of the implied volatility of at-the-money straddles.